#advancedanalytics — Public Fediverse posts

Missing values are a common challenge in data analysis, and properly reporting them is a critical step in understanding your data. By examining the patterns and proportions of missing values, you can assess the potential impact on your analysis and decide how to handle them effectively.

The attached image, created using the VIM package in R, illustrates the proportion of missing values across variables.

More: http://eepurl.com/gH6myT

#advancedanalytics #data #package #datasciencecourse

I recently discovered the tidyplots package in R, and it’s impressive how effortlessly it enables you to create beautiful, publication-ready plots.

The example visualizations shown here were created by the package author, Jan Broder Engler, and are featured on the tidyplots website: https://jbengler.github.io/tidyplots/

Click this link for detailed information: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r

#statisticsclass #datavisualization #advancedanalytics #rprogramminglanguage #visualanalytics #package #tidyverse

If you're a Stata user, you should switch to R now!

Thinking about switching to R? Check out my online course for absolute beginners in R programming.

Click this link for detailed information: https://statisticsglobe.com/online-course-r-introduction

#advancedanalytics #data #package #datasciencecourse #statisticsclass #rprogramminglanguage

Standard deviation is one of the most important concepts in statistics, providing a way to measure how spread out data points are around the mean. It helps in understanding data variability, which is critical for interpreting trends and patterns.

Image credit to Wikipedia: https://en.wikipedia.org/wiki/Standard_deviation#/media/File:MultivariateNormal.png

Interested in learning further? Check out my online course on Statistical Methods in R. Check out this link for more details: https://statisticsglobe.com/online-course-statistical-methods-r

#dataanalytic #dataanalytics #advancedanalytics

Basic boxplots are often not the best way to visualize your data! They can hide important information, such as the distribution of individual data points or group-specific differences.

The attached visual showcases several ways to enhance boxplots.

All of these examples were created using ggplot2 and extensions in R.

Click this link for detailed information: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r

#statisticsclass #datavisualization #advancedanalytics #rprogramminglanguage #visualanalytics #package

Combining Principal Component Analysis (PCA) with k-means Clustering in R can significantly enhance your data analysis by reducing dimensionality and improving clustering performance.

Check out my article created with Cansu Kebabci: https://statisticsglobe.com/pca-before-k-means-clustering-r

I've also created a video: https://www.youtube.com/watch?v=nzhSjOKSGC8

Furthermore, I offer an extensive online course on PCA: https://statisticsglobe.com/online-course-pca-theory-application-r

#datasciencetraining #bigdata #advancedanalytics #datasciencecourse

Principal Component Analysis (PCA) before Linear Regression can greatly enhance your data analysis process.

By incorporating PCA before performing linear regression, you can streamline your analysis pipeline and build more robust models that capture the essential relationships within your data.

I've developed an in-depth course on PCA theory and its application in R programming.

Further details: https://statisticsglobe.com/online-course-pca-theory-application-r

#pythontraining #datascientists #data #bigdata #advancedanalytics